Prediction of ADRs using NMF and Weighted NMFs
Arshad Ahamed Shajahan, Sai Shasank Dandu1
1 School of Mathematical and Statistical Sciences, University of Galway
Background and Problem
Adverse drug reactions (ADRs) are a common problem in clinical and pharmacovigilance research and can lead to serious patient harm and biased conclusions if modelled poorly. Sparse, noisy, and highly imbalanced drug–ADR data often cause standard machine learning methods to perform no better than naïve frequency‑based approaches, unless appropriate low‑rank and kernel‑based methods are used with clear assumptions. [^1]: https://doi.org/10.1093/bioadv/vbae009
Objectives of Project
- Explain the ADR profile prediction problem and its statistical challenges in imbalanced, noisy health data.
- Explore low‑rank and kernel‑based methods (e.g. NMF+kernel regression and alternatives) for ADR prediction.
- Apply these methods to updated drug–ADR datasets with additional molecular and interaction features.
Data Sources and Datasets
The syntax in this poster template and the posterdown package uses the same workflow approach as the R Markdown you know and love.
You can even use the bibliography the same way: Our data were taken from a cluster-randomised trial 1, available from the Irish Social Science Data Archive.
Early Results / Descriptive Statistics of Datasets
Usually you want to have a nice table displaying some important results that you have calculated. In posterdown this is as easy as using the kable table formatting you are probably use to as per typical R Markdown formatting.
You can reference tables like so: Table 1. Some basic summaries of the dataset are below:
| Sepal.Length | Sepal.Width | Petal.Length | Petal.Width |
|---|---|---|---|
| 5.1 | 3.5 | 1.4 | 0.2 |
| 4.9 | 3.0 | 1.4 | 0.2 |
| 4.7 | 3.2 | 1.3 | 0.2 |
| 4.6 | 3.1 | 1.5 | 0.2 |
| 5.0 | 3.6 | 1.4 | 0.2 |
| 5.4 | 3.9 | 1.7 | 0.4 |
| 4.6 | 3.4 | 1.4 | 0.3 |
| 5.0 | 3.4 | 1.5 | 0.2 |
| 4.4 | 2.9 | 1.4 | 0.2 |
| 4.9 | 3.1 | 1.5 | 0.1 |
Figure 1, and Figure 2 below show the patterns in our dataset. Make sure that all the details in your plots will be legible when printed (legend text, axis text, and any labels)
Figure 1: Great figure!
Figure 2: Amazing, right?!
You can even make your plots interactive for the HTML version of the poster. You can use the HTML poster for the presentation session, and the PDF poster will be printed - so be sure the static version looks okay.
Figure 3: Amazing, right?!
Next Project Steps
We plan to conduct further analysis using:
We will use the plasticanalysis package for this.
GitHub
The code and datasets for this project can be viewed at our GitHub repository here: https://github.com/